Person: AK, AYÇA
Loading...
Email Address
Birth Date
Research Projects
Organizational Units
Job Title
Last Name
AK
First Name
AYÇA
Name
8 results
Search Results
Now showing 1 - 8 of 8
Publication Metadata only Fiber optik uygulamaları(Proje Kitabı, 2013-12-01) AK, AYÇA; Ak A.Publication Metadata only SMC controller design for DC motor speed control applications and performance comparison with FLC, PID and PI controllers(Springer, 2023-01-01) AK, AYÇA; OYMAN SERTELLER, NECİBE FÜSUN; Rahmatullah R., Ak A., Oyman Serteller N. F.Publication Metadata only Fiber optic training program with intensive experiments using both real laboratory and simulation environments(WILEY, 2019) SARIKAŞ, ALİ; Aydin, Serkan; Sarikas, Ali; Ak, Ayca; Yayla, Ayse; Kesen, Ugur; Oral, BekirThis paper highlights a fiber optic training program developed according to the occupational competencies using real and simulation platforms to train young people aged between 15 and 24. The most important objective is to overcome the shortages of fiber optic employees by providing training qualifications accredited by the Fiber Optic Association. This training program was designed on three levels, and participants were tested at the end of each level. Successful participants continued with a higher level of training. Theoretical knowledge was given to the participants at the first two levels and extensive practical applications were done. At the third level, computer networks trainings were provided to identify the much more fiber optic network modules by using simulation software tool. The training program includes installation of DVB-X (Digital Video Broadcast - satellite, cable) and the FFT-X (fiber-to-the - home, building, curb) devices that have a fiber optic cable infrastructure, and point-to-point line measurements. This training program differs from similar programs due to the inclusion of effective real laboratory, simulation platform, and field practices. It is significantly found that this training program supported by more extensive real experiments and simulations besides of theoretical education increases the technical qualifications and satisfaction ratio of the participants.Publication Metadata only Motor imagery EEG signal classification using image processing technique over GoogLeNet deep learning algorithm for controlling the robot manipulator(ELSEVIER SCI LTD, 2022) AK, AYÇA; Ak, Ayca; Topuz, Vedat; Midi, IpekControlling of a robotic arm using a brain-computer interface (BCI) is one of the most impressive applications. In this study, a novel method for the classification of motor imaging (MI) electroencephalography (EEG) signals are proposed for BCI. EEG signals are divided into three secondary tables, which were converted into spectrogram images. After applying the spectrogram method, the obtained images are divided into folder structures and deep learning is performed. In the deep learning stage, 400 images are obtained for each task as input to the Goo-gLeNet. After the deep learning completed, the presented system has been tested to imagine up, down, left and right movement to control the movement of the robot arm. It is observed that the robot arm performs the desired movement over 90% accuracy.Publication Metadata only ROBOT TRAJECTORY TRACKING WITH ADAPTIVE RBFNN-BASED FUZZY SLIDING MODE CONTROL(KAUNAS UNIV TECHNOLOGY, 2011) AK, AYÇA; Ak, Ayca Gokhan; Cansever, Galip; Delibasi, AkinDue to computational burden and dynamic uncertainty, the classical model-based control approaches are hard to be implemented in the multivariable robotic systems. In this paper, a model-free fuzzy sliding mode control based on neural network is proposed. In classical sliding mode controllers, system dynamics and system parameters are required to compute the equivalent control. In Radial Basis Function Neural Network (RBFNN) based fuzzy sliding mode control, a RBFNN is developed to mimic the equivalent control law in the Sliding Mode Control (SMC). The weights of the RBFNN are changed for the system state to hit the sliding surface and slide along it with an adaptive algorithm. The initial weights of the RBFNN are set to zero and then tuned online, no supervised learning procedures are needed. In the proposed method, by introducing the fuzzy concept to the sliding mode and fuzzifying the sliding surface, the chattering can be alleviated. The proposed method is implemented on industrial robot (Manutec-r15) and compared with a PID controller. Experimental studies carried out have shown that this approach is a good candidate for trajectory tracking applications of industrial robot.Publication Metadata only SMC controller design for DC motor speed control applications and performance comparison with FLC, PID and PI controllers(2023-01-01) AK, AYÇA; Rahmatullah R., AK A., serteller N. F. O.Sliding Mode Control (SMC), which is built on the variable structure control (VSC) algorithm, is a robust and non-linear control method that can provide the desired dynamic behaviour for the system to be controlled despite external and internal disturbances and uncertainties. The SMC method can be successfully implemented in the control of high-dimensional nonlinear systems operating under uncertain conditions due to its high accuracy and simplicity of application. In this MATLAB/Simulink based study; the SMC method is applied to the speed control of a DC motor. For this purpose, firstly, the dynamic model of DC motor and the mathematical model of the SMC have been designed and transferred to the Simulink environment. The performance of the SMC system has been examined under different loading conditions applied to the motor. In addition, the effects of changing the SMC parameters on the sliding surface, chattering and motor dynamic behaviours have been investigated. In order to evaluate the success of the SMC topology in DC motor control application, Fuzzy Logic Control, PID and PI control methods were applied on the same motor and their performances were compared with the SMC method.Publication Metadata only İnme hastaları için eeg si̇nyalleri̇ ile kontrol edi̇len beyi̇n bi̇lgi̇sayar arayüzü geli̇şti̇rme(2023-12-18) AK, AYÇA; TOPUZ, VEDAT; MİDİ, İPEK; Ersoy S. D., Ak A., Topuz V., Yardımcı G., Midi İ.Publication Metadata only Sliding Mode Controller Design Using Fuzzy Sliding Surface for Flexible Joint Manipulator(TAYLOR & FRANCIS LTD) AK, AYÇA; Ak, AycaThis paper focuses on designing a new robust controller for flexible joint manipulator using time-varying sliding surface. The sliding mode control is thought to be a powerful choice to overcome nonlinear and parametric uncertainties of flexible joint robot manipulators. The basic idea of the proposed control method is based on fuzzifying the sliding surface. Mamdani type fuzzy controller is used for this purpose. The results have demonstrated that the designed control method has superior performance. It is observed that the proposed control algorithm continues to be efficient in the presence of disturbances. In addition, the results are compared with both fuzzy sliding mode control in which the switching control gain was adjusted by fuzzy logic and the classical sliding mode control. It is observed that the method of adjusting the sliding surface with fuzzy logic follows the desired trajectory with less error than other methods. At the same time, the chattering is eliminated by the proposed method.